Sleep Staging in Patients with Suspected Sleep Apnea Using Wearables and Deep Learning
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Author
Aguet, Clémentine
Constantin, Loris
Baty, Florent
Boesch, Maximilian
Renevey, Philippe
Ferrario, Damien
Lemay, Mathieu
Brutsche, Martin
Braun, Fabian
DOI
10.1515/bmt-2025-1001
Abstract
Methods The initial deep learning model combined residual and temporal convolutional layers to capture intricate and long-range dependencies. An iterative multi-stage training approach utilized raw ECG and PPG signals for robust feature learning. A lightweight version using inter-beat intervals was developed for deployment in memory-constrained systems. Both models were evaluated on a cohort of 171 patients with suspected sleep apnea. Participants underwent in-hospital PSG alongside simultaneous recording of reflectance PPG and 3-D accelerometer signals using CSEM’s wrist-worn wearable device.
Results Compared to PSG, the original deep learning model achieved a median accuracy of 80.2% with a Cohen’s Kappa of 0.71 in classifying the four sleep stages: wakefulness, light sleep (S1+S2), deep sleep (S3), and rapid eye movement (REM). It showed low median errors of 10.0 min for total sleep time (TST) and 1.89% for sleep efficiency. The lightweight model performed comparably well (median accuracy 80.1%, Cohen’s Kappa 0.70), with slightly improved sleep metrics (median TST error 8.5 min, median SE error 1.5%).
Conclusion These findings support accurate, scalable, and cost-effective sleep monitoring. The simplified model maintained performance, highlighting potential for efficient wearable implementation without compromising reliability. Future work should enhance wake detection, possibly by incorporating accelerometer-derived motion. Overall, such a PPG-based solution underscores the feasibility of unobtrusive, long-term sleep monitoring at patient’s homes.
Publication Reference
BMT 2025, Muttenz (Switzerland)
Year
2025-09-11